Survival Analysis Using A Multi-Objective Evolutionary Algorithm

@InProceedings{setzkorn:2005:CIMED,
author = "C. Setzkorn and A. F. Taktak and B. E. Damato",
title = "Survival Analysis Using A Multi-Objective Evolutionary
Algorithm",
booktitle = "Proceedings of the 2nd International Conference on
Computational Intelligence in Medicine and Healthcare -
CIMED",
year = "2005",
keywords = "genetic algorithms, genetic programming, evolutionary
algorithms, survival analysis",
pages = "224--230",
address = "Costa da Caparica, Lisbon, Portugal",
month = "29 " # jun # "-1 " # jul,
URL = "http://repository.liv.ac.uk/id/eprint/1194537",
abstract = "proposes a multi-objective evolutionary algorithm for
the extraction of radial basis function networks from
survival data. This type of artificial neural network
has a simpler structure than, for example, the
multi-layer perceptron, which has already been used for
survival analysis. The simpler structure of radial
basis function networks allows a faster model
extraction and better interpretation of the parameters
of the model. Multi-objective evolutionary algorithms
have several advantages over other optimisation methods
such as back-propagation. They can, for example, cope
better with feature interactions and noisy data.
Furthermore, they are capable of optimising several
objectives. This is important in the context of model
extraction, which is a multi-objective problem. It has
at least two objectives, which are the extraction of
(1) accurate and (2) simple models from data. Accurate
models are required to achieve good predictions. Model
simplicity is important to prevent overfitting, improve
the transparency of the models, and to save
computational resources. The proposed approach is
applied to two datasets. The extracted models achieve
good predictive performance.",
notes = "CIMED2005
http://www.uninova.pt/cimed2005/Programme%20Book.pdf",
}